{"title":"当某些双因素相互作用很重要时,使用最小像差标准选择基线设计","authors":"Anqi Chen, Cheng-Yu Sun, Boxin Tang","doi":"10.1080/24754269.2020.1867795","DOIUrl":null,"url":null,"abstract":"ABSTRACT This article considers the problem of selecting two-level designs under the baseline parameterisation when some two-factor interactions are important. We propose a minimum aberration criterion, which minimises the bias caused by the non-negligible effects. Using this criterion, a class of optimal designs can be further distinguished from one another, and we present an algorithm to find the minimum aberration designs among the D-optimal designs. Sixteen-run and twenty-run designs are summarised for practical use.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"5 1","pages":"95 - 101"},"PeriodicalIF":0.7000,"publicationDate":"2021-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2020.1867795","citationCount":"2","resultStr":"{\"title\":\"Selecting baseline designs using a minimum aberration criterion when some two-factor interactions are important\",\"authors\":\"Anqi Chen, Cheng-Yu Sun, Boxin Tang\",\"doi\":\"10.1080/24754269.2020.1867795\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT This article considers the problem of selecting two-level designs under the baseline parameterisation when some two-factor interactions are important. We propose a minimum aberration criterion, which minimises the bias caused by the non-negligible effects. Using this criterion, a class of optimal designs can be further distinguished from one another, and we present an algorithm to find the minimum aberration designs among the D-optimal designs. Sixteen-run and twenty-run designs are summarised for practical use.\",\"PeriodicalId\":22070,\"journal\":{\"name\":\"Statistical Theory and Related Fields\",\"volume\":\"5 1\",\"pages\":\"95 - 101\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2021-01-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/24754269.2020.1867795\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Statistical Theory and Related Fields\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.1080/24754269.2020.1867795\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistical Theory and Related Fields","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1080/24754269.2020.1867795","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
Selecting baseline designs using a minimum aberration criterion when some two-factor interactions are important
ABSTRACT This article considers the problem of selecting two-level designs under the baseline parameterisation when some two-factor interactions are important. We propose a minimum aberration criterion, which minimises the bias caused by the non-negligible effects. Using this criterion, a class of optimal designs can be further distinguished from one another, and we present an algorithm to find the minimum aberration designs among the D-optimal designs. Sixteen-run and twenty-run designs are summarised for practical use.